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All Journal Bulletin of Electrical Engineering and Informatics Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi PROtek : Jurnal Ilmiah Teknik Elektro IJIS - Indonesian Journal On Information System Jiko (Jurnal Informatika dan komputer) Patria Artha Technological Journal Jurnal Teknik Informatika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Dialektika Informatika (Detika) BAKTI : Jurnal Pengabdian Kepada Masyarakat RENATA Jurnal Pengabdian Masyarakat Kita Semua BAKTI : Jurnal Pengabdian Kepada Masyarakat
Tempola, Firman
Universitas Khairun
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Transportation Other

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AN LSTM-BASED APPROACH FOR INDONESIAN NEWS CATEGORIZATION: PERFORMANCE ANALYSIS OF HYPERPARAMETER TUNING AND PREPROCESSING Iwan La Udin; Firman Tempola; Abdul Mubarak; Muhammad Sabri Ahmad; Munazat Salmin; Saiful Do Abdullah
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10783

Abstract

News disseminated through internet-based systems or news portals is generally classified into specific categories, such as politics, sports, economy, entertainment, technology, health, and others. Currently, this categorization is performed manually, requiring a thorough reading of the entire news content. To address this inefficiency, an automatic classification system for Indonesian news articles is necessary to categorize them based on predetermined categories. This research employs a Natural Language Processing (NLP) approach and implements the Long Short-Term Memory (LSTM) architecture. The study was conducted using several testing scenarios, including (1) hyperparameter tuning of the learning rate to 0.01 and 0.001, (2) the application and omission of stemming, and (3) various dataset comparison ratios of 60:40, 70:30, 80:20, and 90:10. The evaluation utilized a dataset of 10,000 articles across 5 categories and was measured using accuracy, precision, recall, and f-measure metrics. From the three scenarios, seven training models were generated. The second model, with a learning rate of 0.001, without stemming, and a 90:10 dataset ratio, achieved the highest accuracy of 90.7%, with average precision, recall, and f-measure scores of 91%. The third and fourth models, which applied stemming, did not demonstrate a performance improvement, both yielding an accuracy of 89%. The fifth model, with a 60:40 dataset ratio, produced an accuracy of 90%, while the sixth and seventh models, with 70:30 and 80:20 ratios, resulted in accuracies of 79% and 88%, respectively.
Co-Authors Abd Hakim Husen Abdul Mubarak Abdul Mubarak ACHMAD FUAD Achmad Fuad1 Aina Musdholifah Akbar Riodano Laisouw Amal Khairan Amal Khairan Anas Anas Anas Anas, Anas Andi Sitti Nur Afiah Assaf Arief, Assaf Azim Hisyam Baksir Faisal F Taran Ibnu, Wirda Sintia Ishak, Gunawan Iwan La Udin Jamil, Moh. Kapita, Syarifuddin N Latif, Nurdewi Lilik Sumaryanti Moh. Jamil Mubarak, Abdul Muhammad Sabri Ahmad Muhammad, Miftah Munazat Salmin Putra, Surya Adi Pratama Ranjasmara, Muhammad Rendy Pratama Retantyo Wardoyo Rosihan Rosihan Rosihan Rosihan, Rosihan rosihan, rosihan Saiful Do Abdullah Salkin Lutfi Salkin Lutfi Samlan Mahdi Sirajuddin, Hairil Kurniadi Soesanti Soesanti Sujabtri Kabau, Haan Sukmawati, Hesti Sukril I Wahid Syarifuddin N. Kapita Umar, Kiki Amelia Wahid, Sukril I